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Signature decomposition method applying to pair trading

Abstract:
High-frequency quantitative trading strategies have long been of significant interest in futures market. While advanced statistical arbitrage and deep learning enhance high-frequency data processing, they diminish opportunities for traditional methods and yield less interpretable, unstable strategies. Consequently, developing stable, interpretable quantitative strategies remains a priority in futures markets. In this study, we propose a novel pair trading strategy by leveraging the mathematical concept of path signature which serves as a feature representation of time series. Specifically, the path signature is decomposed into two new indicators: the path interactivity indicator segmented signature and the directional indicator covariation of increments, which serve as double filters in strategy design. Empirical experiments using minute-level futures data show our strategy significantly outperforms traditional pair trading, delivering higher returns, lower maximum drawdown, and higher Sharpe ratio. The proposed method enhances interpretability and robustness while maintaining strong returns, demonstrating the potential of path signatures in financial trading.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1002/fut.70075

Authors

More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Oxford college:
St Peter's College
Role:
Author
ORCID:
0000-0001-5299-5730
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Oxford college:
Exeter College
Role:
Author
ORCID:
0000-0002-5508-8009


Publisher:
Wiley
Journal:
Journal of Futures Markets More from this journal
Volume:
46
Issue:
3
Article number:
582-603
Publication date:
2025-12-14
Acceptance date:
2025-12-02
DOI:
EISSN:
1096-9934
ISSN:
0270-7314


Language:
English
Keywords:
Pubs id:
2353464
UUID:
uuid_4fcdc68f-fa2d-486d-a9ff-01e5aeabd325
Local pid:
pubs:2353464
Source identifiers:
W4417313807
Deposit date:
2026-01-21
ARK identifier:

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